A technical review of one production or near-production AI workflow. It tests what the architecture, traces and evaluations can actually prove, then turns the gaps into prioritized remediation and deployment choices.
how the review moves
frame
AI workflow, deployment decision and the buyer questionnaire
inspect
architecture, traces and available tests
qualify
strong evidence, gaps and risks
decide
answers, priorities and recommendation
you get
system and data
- architecture and trust-boundary map
- LLM traces, tool calls, logs and replay inspection
- data-use, retention, and hosting summary across managed, hybrid, customer-VPC, or self-hosted deployment as applicable
controls and gaps
- evaluation baseline and gaps
- human-oversight and failure-mode review
- the ten most important evidence gaps, ranked
decision pack
- buyer-facing technical answers you can paste into a questionnaire
- prioritized remediation backlog
- a recommendation to hold deployment, remediate before release, or proceed with the buyer review
best fit when
a product or platform team owns an LLM or agent workflow that can already be demonstrated
an enterprise deal, deployment, or security review currently blocked
a decision date inside 90 days
what follows
3 to 5 weeks to turn real failures into evaluations, regression tests and release gates for the AI workflow
When the review prescribes a build rather than a document, this is what that work looks like: production AI platforms and agentic systems